Internet of Things (IoT) and Machine Learning Model of Plant Disease Prediction–Blister Blight for Tea Plant

Liu, Zhiyan; Bashir, Rab Nawaz; Iqbal, Salman; Shahid, Malik Muhammad Ali; Tausif, Muhammad; Umer, Qasim

doi:10.1109/access.2022.3169147

Cited by 72 publications

(27 citation statements)

References 28 publications

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“…Guava is also included. The compilation included both pictures collected from the Internet as well as those shot by the contributors themselves [19,20]. The images were rotated, modified affinely & perspectively, and had their intensities changed.…”

Section: Literature Surveymentioning

confidence: 99%

ECDBMFA: Design of an Ensemble engine for Crop Disease detection via Bioinspired Multidomain Feature Analysis

Malathi¹,

Sheela²

2023

Preprint

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Crop disease detection models require integration of multimodal tasks including contextual segmentation, feature representation & selection, classification & post-processing operations. A wide variety of machine learning & deep learning techniques are proposed by researchers to design such models, but most of them are either highly complex, or have limited efficiency when applied to real-time farm fields. To overcome these issues, this text proposes design of an efficient & novel ensemble engine for crop disease detection via bioinspired multidomain feature analysis. The proposed model uses Fuzzy Saliency Maps for identification of contextual disease regions, which are represented into multidomain feature sets. These sets use Fourier, Gabor, Convolutional, Wavelet and Cosine operations, which assist in extraction of multiple high-density features. The extracted features are reduced via a Bacterial Foraging Optimization (BFO) Model, which retains only higher variance feature sets. The retained features are classified by an ensemble model, that combines Naïve Bayes (NB), Multilayer Perceptron (MLP), Logistic Regression (LR), k Nearest Neighbors (kNN) and Support Vector Machine (SVM) classifiers. These classifiers are combined using a boosting model, that assists in retaining unique-correct class training information sets. Due to which the proposed model is able to improve the classification accuracy for Wheat, Tomato, Rice & Corn diseases by 9.4%, while improving precision by 3.5%, recall by 5.9% and reducing delay by 4.3% when compared with existing classification models under similar disease types.

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Section: Literature Surveymentioning

confidence: 99%

ECDBMFA: Design of an Ensemble engine for Crop Disease detection via Bioinspired Multidomain Feature Analysis

Malathi¹,

Sheela²

2023

Preprint

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“…The dataset consisted of 36,258 images. A more recent study that used Multiple Linear Regression (MLP) reported 91% accuracy in identifying blister blight in tea plants [34], while another recent study reported the use of CNN algorithm to detect 13 different diseases achieved precision of 96.3% [35].…”

Section: A Related Workmentioning

confidence: 99%

A Machine Learning Hybrid Approach for Diagnosing Plants Bacterial and Fungal Diseases

BaniMustafa¹,

Qattous²,

Ghabeish³

et al. 2023

IJACSA

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Bacterial and Fungal diseases may affect the yield of stone fruit and cause damage to the Chlorophyll synthesis process, which is crucial for tree growth and fruiting. However, due to their similar visual shot-hole symptoms, novice agriculturalists and ordinary farmers usually cannot identify and differentiate these two diseases. This work investigates and evaluates the use of machine learning for diagnosing these two diseases. It aims at paving the way toward creating a generic deep learning-based model that can be embedded in a mobile phone application or in a web service to provide a fast, reliable, and cheap diagnosis for plant diseases which help reduce the excessive, unnecessary, or improper use of pesticides, which can harm public health and the environment. The dataset consists of hundreds of samples collected from stone fruit farms in the north of Jordan under normal field conditions. The image features were extracted using a CNN algorithm that was pre-trained with millions of images, and the diseases were identified using three machine learning classification algorithms: (1) K-nearest neighbour (KNN); (2) Stochastic Gradient Descent (SGD); and (3) Random Forests (RF). The resulting models were evaluated using 10-fold crossvalidation, with CNN-KNN achieving the best AUC performance with a score of 98.5%. On the other hand, the CNN-SGD model performed best in Classification Accuracy (CA) with a score of 93.7%. The results shown in the Confusion Matrix, ROC, Lift, and Calibration curves also confirmed the validity and robustness of the constructed models.

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“…This action will provide power to the fan, enabling it to start and exhaust air from the greenhouse or enclosed area. Similarly, if the gas concentration falls below the threshold, turn off the GPIO pin connected to the fan, stopping the fan and conserving power [11].…”

Section: Ventilation Systemmentioning

confidence: 99%

Smart Greenhouse and Warehouse Monitoring with Disease Detection using Machine Learning

Savitha,

Rai,

Singh

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

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Global climatic changes in the recent era have resulted in a significant global food shortage. Where there is a vast population and a diverse agricultural sector, food security is a major problem. Growing competition for land, water, and energy will undermine our capital food production, and the effect of climate change will continue to be a challenge. The integrated greenhouse and warehouse provide quality and sustainable food production. In this paper an integrated green house with warehouse monitoring hardware implementation, with novel feature of customized disease detection with convolution neural network (CNN) algorithm is presented. The real time monitoring of data of different sensors are displayed on the screen/ dashboard developed by the android application. This aids farmers in making wise choices on crop management and disease detection of greenhouse crops and effective monitoring.

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Internet of Things (IoT) and Machine Learning Model of Plant Disease Prediction–Blister Blight for Tea Plant

Cited by 72 publications

References 28 publications

ECDBMFA: Design of an Ensemble engine for Crop Disease detection via Bioinspired Multidomain Feature Analysis

ECDBMFA: Design of an Ensemble engine for Crop Disease detection via Bioinspired Multidomain Feature Analysis

A Machine Learning Hybrid Approach for Diagnosing Plants Bacterial and Fungal Diseases

Smart Greenhouse and Warehouse Monitoring with Disease Detection using Machine Learning

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